Bring source data in
Start with file uploads and source links, then support signed URLs, manifests, storage paths, and customer-managed runtimes without changing the review workflow.
source file, URL, storage
DATASCREEN·AI DATA INTEGRITY
Review AI training and evaluation data wherever it lives.
Upload files, preserve source links, and support pilot paths into signed URLs, storage exports, and customer-managed runtimes. Datascreen surfaces data issues, source context, and review records before datasets reach model workflows.
Platform
The platform for reviewing AI data wherever teams keep it.
Datascreen gives data and platform teams a structured way to inspect files, source links, storage exports, external datasets, and internal training or eval sets before they feed AI systems.
Surface data issues
Prioritize rows, clusters, and source areas that may carry hidden controls, leaked answer keys, construction residue, or conflicting supervision.
surface issues worth review
Preserve source context
Keep file, object, row, field, source label, neighborhood, and change context attached so reviewers can understand what happened.
context source-aware
Create review records
Keep an internal scan artifact and a customer-facing record of what was scanned, what surfaced, what changed, and what limits remain.
report review record
Use cases
Choose the AI data workflow you need to review.
Each use case maps to a product workflow: choose the problem, point Datascreen at the source, review the findings, inspect evidence, and export a record.
01
Training Data Integrity
Review training data before it changes model behavior.
02Eval Set Leakage
Check whether evaluation data can still be trusted before results are reported.
03External Data Integrity
Inspect public, vendor, or third-party datasets before they enter AI workflows.
04Data Poisoning
Surface adversarially useful rows and trigger patterns before they reach training data.
05Dataset Changes
See what changed before retraining, re-evaluating, or appending new data.
06Synthetic Data Risk
Review synthetic-heavy data before recursive patterns and low-diversity rows accumulate.
What unscreened data can become
Four failure modes that reach the model.
Some are ordinary pipeline accidents. Others are adversarially useful residue. The common thread: they can slip past visual review and survive long enough to affect training runs, evaluations, internal reports, or audits.
A benchmark row enters the training set.
A held-out evaluation example appears verbatim, or near-verbatim, in a fine-tuning dataset.
→The next eval report can look better than the model really is. The number moved, but the data pipeline may be the reason.
A zero-width payload survives visual review.
Invisible characters carry instruction-shaped text inside ordinary-looking rows.
→The row deserves review because hidden structure can change how training data is parsed, displayed, or learned.
Refusal patterns leak into benign examples.
Upstream-model refusals on ordinary topics — basic chemistry, weekend plans, photosynthesis — remain in the training data.
→Users can hit walls on normal questions, and the cause may be buried in the dataset instead of the model code.
Source annotations survive preprocessing.
Bracketed gold labels, "[ANSWER]" tokens, and pipeline metadata persist in the response field.
→The model memorizes the test surface rather than generalizing. Evaluation gains evaporate on new distributions.
Design partner conversations
Reviewing AI data before a model run?
We are looking for ML data and platform teams that inspect fine-tuning, eval, or external datasets before training. Bring a dataset, a storage workflow, a review process, or a failure mode you want surfaced earlier.